Creating Value on the Inside: Design‐Driven Innovation to Create New Meanings for Internal Stakeholders
Bibliographic record
Abstract
Design‐driven innovation (DDI) is an approach to innovation that focuses on creating new meanings for the products and services a company offers. DDI differs from other forms of innovation, which are typically more so driven by the development of breakthrough technologies or on addressing current market needs. It is argued that DDI is an effective approach to creating value and promoting the growth of a company. Research regarding DDI has largely focused on the outcomes for end‐users or overall company growth. While these outcomes are important, a lot goes on behind the scenes to successfully deliver them. Internal stakeholders such as managers and employees are responsible for delivering these outcomes, and research regarding value creation for them is currently limited. This paper serves as a starting point for further research by presenting a critical literature review that investigates how DDI could be a catalyst for innovation and growth through the creation of new meanings for internal stakeholders involved in the development of products and services. The result of this literature review is the identification of possible areas for intervention and a proposal for further primary research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".